Ranking results
model = "LR LASSO"
metrics = c("Accuracy", "AUC")
knitr::kable(table_nonranking(nonranking_results, model, metrics))
| FCBF |
9.8 +- 0.841 |
0.977 +- 0.006 |
0.969 +- 0.017 |
| QuiPT 0.01 |
49.9 +- 9.273 |
0.93 +- 0.006 |
0.875 +- 0.032 |
| QuiPT 0.05 |
75.4 +- 12.058 |
0.932 +- 0.006 |
0.9 +- 0.028 |
| Chi-squared 0.01 |
573.3 +- 60.234 |
0.927 +- 0.005 |
0.899 +- 0.016 |
| Chi-squared 0.05 |
812.9 +- 71.2 |
0.924 +- 0.005 |
0.887 +- 0.016 |
xtable(table_nonranking(nonranking_results, model, metrics),
label = paste0(exp_prefix, "_nonranking_LR_table"),
caption = paste0(experiment_name, " - averaged filtering results for LASSO classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun 6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
\hline
& n-kmers selected & Accuracy & AUC \\
\hline
FCBF & 9.8 +- 0.841 & 0.977 +- 0.006 & 0.969 +- 0.017 \\
QuiPT 0.01 & 49.9 +- 9.273 & 0.93 +- 0.006 & 0.875 +- 0.032 \\
QuiPT 0.05 & 75.4 +- 12.058 & 0.932 +- 0.006 & 0.9 +- 0.028 \\
Chi-squared 0.01 & 573.3 +- 60.234 & 0.927 +- 0.005 & 0.899 +- 0.016 \\
Chi-squared 0.05 & 812.9 +- 71.2 & 0.924 +- 0.005 & 0.887 +- 0.016 \\
\hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for LASSO classifier.}
\label{exp1_1m_300s_50p_nonranking_LR_table}
\end{table}
model = "1-NN"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
| FCBF |
9.8 +- 0.841 |
0.943 +- 0.014 |
0.893 +- 0.018 |
| QuiPT 0.01 |
49.9 +- 9.273 |
0.859 +- 0.015 |
0.599 +- 0.017 |
| QuiPT 0.05 |
75.4 +- 12.058 |
0.882 +- 0.01 |
0.607 +- 0.018 |
| Chi-squared 0.01 |
573.3 +- 60.234 |
0.895 +- 0.006 |
0.558 +- 0.013 |
| Chi-squared 0.05 |
812.9 +- 71.2 |
0.89 +- 0.007 |
0.527 +- 0.006 |
xtable(table_nonranking(nonranking_results, model, metrics),
label = paste0(exp_prefix, "_nonranking_1NN_table"),
caption = paste0(experiment_name, " - averaged filtering results for 1-nearest neighbor classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun 6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
\hline
& n-kmers selected & Accuracy & AUC \\
\hline
FCBF & 9.8 +- 0.841 & 0.943 +- 0.014 & 0.893 +- 0.018 \\
QuiPT 0.01 & 49.9 +- 9.273 & 0.859 +- 0.015 & 0.599 +- 0.017 \\
QuiPT 0.05 & 75.4 +- 12.058 & 0.882 +- 0.01 & 0.607 +- 0.018 \\
Chi-squared 0.01 & 573.3 +- 60.234 & 0.895 +- 0.006 & 0.558 +- 0.013 \\
Chi-squared 0.05 & 812.9 +- 71.2 & 0.89 +- 0.007 & 0.527 +- 0.006 \\
\hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for 1-nearest neighbor classifier.}
\label{exp1_1m_300s_50p_nonranking_1NN_table}
\end{table}
model = "16-NN"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
| FCBF |
9.8 +- 0.841 |
0.912 +- 0.002 |
0.954 +- 0.016 |
| QuiPT 0.01 |
49.9 +- 9.273 |
0.9 +- 0 |
0.759 +- 0.034 |
| QuiPT 0.05 |
75.4 +- 12.058 |
0.9 +- 0 |
0.769 +- 0.039 |
| Chi-squared 0.01 |
573.3 +- 60.234 |
0.9 +- 0 |
0.636 +- 0.025 |
| Chi-squared 0.05 |
812.9 +- 71.2 |
0.9 +- 0 |
0.571 +- 0.016 |
xtable(table_nonranking(nonranking_results, model, metrics),
label = paste0(exp_prefix, "_nonranking_16NN_table"),
caption = paste0(experiment_name, " - averaged filtering results for 16-nearest neighbor classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun 6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
\hline
& n-kmers selected & Accuracy & AUC \\
\hline
FCBF & 9.8 +- 0.841 & 0.912 +- 0.002 & 0.954 +- 0.016 \\
QuiPT 0.01 & 49.9 +- 9.273 & 0.9 +- 0 & 0.759 +- 0.034 \\
QuiPT 0.05 & 75.4 +- 12.058 & 0.9 +- 0 & 0.769 +- 0.039 \\
Chi-squared 0.01 & 573.3 +- 60.234 & 0.9 +- 0 & 0.636 +- 0.025 \\
Chi-squared 0.05 & 812.9 +- 71.2 & 0.9 +- 0 & 0.571 +- 0.016 \\
\hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for 16-nearest neighbor classifier.}
\label{exp1_1m_300s_50p_nonranking_16NN_table}
\end{table}
model = "RF (500 trees)"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
| FCBF |
9.8 +- 0.841 |
0.968 +- 0.006 |
0.971 +- 0.016 |
| QuiPT 0.01 |
49.9 +- 9.273 |
0.912 +- 0.004 |
0.855 +- 0.038 |
| QuiPT 0.05 |
75.4 +- 12.058 |
0.913 +- 0.003 |
0.877 +- 0.035 |
| Chi-squared 0.01 |
573.3 +- 60.234 |
0.908 +- 0.003 |
0.908 +- 0.018 |
| Chi-squared 0.05 |
812.9 +- 71.2 |
0.905 +- 0.003 |
0.891 +- 0.024 |
xtable(table_nonranking(nonranking_results, model, metrics),
label = paste0(exp_prefix, "_nonranking_RF500_table"),
caption = paste0(experiment_name, " - averaged filtering results for random forest classifier (500 trees)."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun 6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
\hline
& n-kmers selected & Accuracy & AUC \\
\hline
FCBF & 9.8 +- 0.841 & 0.968 +- 0.006 & 0.971 +- 0.016 \\
QuiPT 0.01 & 49.9 +- 9.273 & 0.912 +- 0.004 & 0.855 +- 0.038 \\
QuiPT 0.05 & 75.4 +- 12.058 & 0.913 +- 0.003 & 0.877 +- 0.035 \\
Chi-squared 0.01 & 573.3 +- 60.234 & 0.908 +- 0.003 & 0.908 +- 0.018 \\
Chi-squared 0.05 & 812.9 +- 71.2 & 0.905 +- 0.003 & 0.891 +- 0.024 \\
\hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for random forest classifier (500 trees).}
\label{exp1_1m_300s_50p_nonranking_RF500_table}
\end{table}
model = "RF (1000 trees)"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
| FCBF |
9.8 +- 0.841 |
0.968 +- 0.006 |
0.971 +- 0.016 |
| QuiPT 0.01 |
49.9 +- 9.273 |
0.912 +- 0.005 |
0.857 +- 0.037 |
| QuiPT 0.05 |
75.4 +- 12.058 |
0.913 +- 0.004 |
0.88 +- 0.035 |
| Chi-squared 0.01 |
573.3 +- 60.234 |
0.907 +- 0.003 |
0.907 +- 0.019 |
| Chi-squared 0.05 |
812.9 +- 71.2 |
0.905 +- 0.003 |
0.891 +- 0.024 |
xtable(table_nonranking(nonranking_results, model, metrics),
label = paste0(exp_prefix, "_nonranking_RF1000_table"),
caption = paste0(experiment_name, " - averaged filtering results for random forest classifier (1000 trees)."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun 6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
\hline
& n-kmers selected & Accuracy & AUC \\
\hline
FCBF & 9.8 +- 0.841 & 0.968 +- 0.006 & 0.971 +- 0.016 \\
QuiPT 0.01 & 49.9 +- 9.273 & 0.912 +- 0.005 & 0.857 +- 0.037 \\
QuiPT 0.05 & 75.4 +- 12.058 & 0.913 +- 0.004 & 0.88 +- 0.035 \\
Chi-squared 0.01 & 573.3 +- 60.234 & 0.907 +- 0.003 & 0.907 +- 0.019 \\
Chi-squared 0.05 & 812.9 +- 71.2 & 0.905 +- 0.003 & 0.891 +- 0.024 \\
\hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for random forest classifier (1000 trees).}
\label{exp1_1m_300s_50p_nonranking_RF1000_table}
\end{table}
plot_ranking_results(ranking_results, "AUC")
ggsave(paste0("plots/", exp_prefix, "_ranking_results_AUC.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_AUC.pdf}\n",
"\\caption{", experiment_name, " - averaged AUC score for each ranking-based k-mer filtering technique.}\n",
"\\label{fig:", exp_prefix,"_ranking_results_AUC}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each ranking-based k-mer filtering technique.}
\label{fig:exp1_1m_300s_50p_ranking_results_AUC}
\end{figure}
plot_ranking_results(ranking_results, "Accuracy")
ggsave(paste0("plots/", exp_prefix, "_ranking_results_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_Accuracy.pdf}\n",
"\\caption{", experiment_name, " - averaged accuracy for each ranking-based k-mer filtering technique.}\n",
"\\label{fig:", exp_prefix,"_ranking_results_Accuracy}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each ranking-based k-mer filtering technique.}
\label{fig:exp1_1m_300s_50p_ranking_results_Accuracy}
\end{figure}
two_models_results <- lapply(ranking_results, function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])
plot_ranking_results(two_models_results, "AUC", ncol=1)
ggsave(paste0("plots/", exp_prefix, "_ranking_results_2models_AUC.pdf"))
Saving 12 x 6 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_2models_AUC.pdf}\n",
"\\caption{", experiment_name, " - averaged AUC for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}\n",
"\\label{fig:", exp_prefix,"_ranking_results_2models_AUC}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_2models_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}
\label{fig:exp1_1m_300s_50p_ranking_results_2models_AUC}
\end{figure}
two_models_results <- lapply(ranking_results, function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])
plot_ranking_results(two_models_results, "Accuracy", ncol=1)
ggsave(paste0("plots/", exp_prefix, "_ranking_results_2models_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_2models_Accuracy.pdf}\n",
"\\caption{", experiment_name, " - averaged accuracy for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}\n",
"\\label{fig:", exp_prefix,"_ranking_results_2models_Accuracy}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_2models_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}
\label{fig:exp1_1m_300s_50p_ranking_results_2models_Accuracy}
\end{figure}
plot_ranking_results_w_nonranking(ranking_results, nonranking_results, "AUC", ncol=3)
ggsave(paste0("plots/", exp_prefix, "_results_AUC.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_AUC.pdf}\n",
"\\caption{", experiment_name, " - averaged AUC score for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
"\\label{fig:", exp_prefix,"_results_AUC.pdf}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_AUC.pdf}
\end{figure}
plot_ranking_results_w_nonranking(ranking_results, nonranking_results, "Accuracy", ncol=3)
ggsave(paste0("plots/", exp_prefix, "_results_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_Accuracy.pdf}\n",
"\\caption{", experiment_name, " - averaged accuracy for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
"\\label{fig:", exp_prefix,"_results_Accuracy.pdf}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_Accuracy.pdf}
\end{figure}
two_models_results_nonranking <- lapply(nonranking_results,
function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])
plot_ranking_results_w_nonranking(two_models_results, two_models_results_nonranking, "AUC", ncol=1)
ggsave(paste0("plots/", exp_prefix, "_results_2models_AUC.pdf"))
Saving 12 x 8 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_2models_AUC.pdf}\n",
"\\caption{", experiment_name, " - averaged AUC score for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
"\\label{fig:", exp_prefix,"_results_2models_AUC}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_2models_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_2models_AUC}
\end{figure}
plot_ranking_results_w_nonranking(two_models_results, two_models_results_nonranking, "Accuracy", ncol=1)
ggsave(paste0("plots/", exp_prefix, "_results_2models_Accuracy.pdf"))
Saving 12 x 8 in image
cat(paste0("\\begin{figure}\n",
"\\centering\n",
"\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_2models_Accuracy.pdf}\n",
"\\caption{", experiment_name, " - averaged accuracy for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
"\\label{fig:", exp_prefix,"_results_2models_Accuracy}\n",
"\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_2models_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_2models_Accuracy}
\end{figure}
